A multiple-input-multiple-output (MIMO) communication system employs multiple transmit antennas in a transmitter and multiple receive antennas in a receiver for data transmission. A MIMO channel formed by the transmit and receive antennas may be decomposed into independent channels, wherein each channel is a spatial sub-channel (or a transmission channel) of the MIMO channel and corresponds to a dimension. The MIMO system can provide improved performance (e.g., increased transmission capacity) if the additional dimensionalities created by the multiple transmit and receive antennas are utilized.
MIMO increases system link robustness and spectral efficiency. To optimize spectral efficiency for MIMO system, many efforts have been made, which can be broadly classified into two categorists: open-loop approaches and closed-loop approaches. The open-loop approaches include spatial multiplexing, space-time coding and the tradeoff between them. The closed-loop approaches focus on maximizing the link capacity, which results in a “water-filling” solution, and on minimizing the weighted MMSE which provides an “inverse water-filling” solution.
In an open-loop MIMO system, the MIMO transmitter has no prior knowledge of the channel condition (i.e., channel state information). As such, space-time coding techniques are usually implemented in the transmitter to combat fading channels. In a closed-loop system, the channel state information (CSI) can be fed back to the transmitter from the receiver, wherein some pre-processing can be performed at the transmitter in order to separate the transmitted data streams at the receiver side.
Such techniques are referred to as beamforming techniques, which provide better performance in desired receiver's directions and suppress the transmit power in other directions. Beamforming techniques are considered for IEEE 802.11n (high throughput WLAN) standard. Closed-loop eigen-beamforming generally provides higher system capacity compared with the closed-loop solution, assuming the transmitter knows the down-link channel. Singular value decomposition (SVD) based eigen-beamforming decomposes the correlated MIMO channel into multiple parallel pipes.
In steered data transmission, the basic principle of the singular value decomposition (SVD)-based eigen-beamforming decomposes the correlated MIMO channel into multiple parallel pipes. FIG. 1 shows a MIMO wireless communication system 100 comprising a transmitter TX and a receiver RX. The transmitter TX includes an FEC (forward error correction) encoder 102 that encodes an input bit stream, a parser 104 that generates Nss number of spatial data streams, Nss number of modulators 106, linear precoder (V) 108 that generates Nt transmit streams, RF unit 110 and Nt transmit antennas 112.
In the unit 102 the source bit stream is encoded by a channel encoder and a puncturer punctures the bits to change the coding rate. The spatial parser 104 separates the data stream into several (one or more) spatial streams. The constellation mapper 106 groups/maps the bits into symbols using a Gray Mapping Rule. The precoder 108 provides steering of the packet using V matrix. In the RF modulator 110, the signal is RF modulated and transmitted through the strongest channel via the transmit antennas 112.
The receiver RX includes Nr receive antennas 120, RF unit 122 that generates Nr received streams, a linear decoder (UH) 124 that generates Nss data streams, Nss demodulators 126 (Nss output streams), de-parser 128 and a decoder 130.
In the receiver RX, the receiving antennas 120 receive the signals, and the received signals are sampled and down-converted to base-band digital signal in the unit 122. The decoder 124 performs linear MIMO detection. The multiple demodulators 126 perform constellation de-mapping that demap the constellation point to soft bit information. The deparser 128 de-multiplexes multiple data streams back to one stream for Viterbi decoding. And, the decoder 130 performs the Viterbi decoding.
Considering the MIMO system transmits Nss number of data streams with Nt transmit antennas and Nr receive antennas (Nss<min(Nr, Nt)), as shown in FIG. 1, then the received signal y at the receiver can be represented as:y=HVx+n  (1)
wherein x is the Nss×1 transmitted signal vector, V is the Nt×Nss right singular value matrix corresponding to the Nss largest eigen-values, H is a Nr×Nt channel response which can be factored using SVD such that H=U D VH, and n is Nr×1 additive noise vector in the channel.
The system in FIG. 1 does not specify how to choose the coding and modulation to transmit. As shown in FIG. 1, in the transmitter TX the information bit stream is first parsed into Nss streams by the parser 104. At the receiver RX, the received signal y is multiplied by the left singular value matrix UH in the linear decoder 124, wherein the received signal after such processing, Xp, can be represented as:Xp=UHy=Dx+UHn.  (2)
To achieve link capacity, eigen-beamforming requires changing the coding and modulation on subcarrier basis, which gives the so-called water-filling solution. However, the complexity is very high for practical implementation. In order to simplify the complexity, several systems have been proposed including adapting coding/modulation across all subcarrier.
In rate adaptation, the mechanism to select one of the multiple available transmission rates at a given time is referred to as link adaptation, rate adaptation, or MCS (modulation coding scheme) adaptation. The effectiveness of the implemented link adaptation scheme can affect the system performance significantly.
Current IEEE 802.11a PHY has 8 transmission rates for rate adaptation. Some proprietary link adaptation schemes have been proposed for WLAN recently. In one case, a receiver-based auto-rate scheme based on the RTS/CTS mechanism is proposed by modifying the IEEE 802.11 standard. The basic idea is: first the receiver estimates the wireless channel quality using a sample of the instantaneous received channel strength at the end of the RTS (Request-To-Send) reception, then select the appropriate transmission rate based on this estimate and feeds back to the transmitter using CTS (Clear-To-Send). In another case, the C/I (carrier to interference ratio) is used as the wireless link quality measurement. All these link adaptation schemes make PHY mode selection based on some pre-computed PHY mode tables corresponding to the link quality measurement. The table is manageable with 8 transmission modes in the current IEEE 802.11a standard.
However, for IEEE 802.11n MIMO-OFDM (orthogonal frequency-division multiplexing) transmission, 126 transmission modes are defined (see S. A. Mujtaba, “TGn Sync Proposal Technical Specification,” a contribution to IEEE 802.11, 11-04-889r7, July 2005, incorporated herein by reference), to fully explore the MIMO wireless channel variation wherein multiple transmission modes are defined for the same transmission rate. In this case, rate selection becomes more complicated. Using similar rate table as in IEEE 802.11a is no longer feasible. As such, there is need for a new way of rate adaptation for MIMO eigen-beamforming.